Experimental study of dynamic loss mechanisms in transonic turbines

该项目将使燃气轮机发动机具有更高的效率和减轻的重量。这些发动机在航空中无处不在，也构成了能源版图中的一大部分，而且还在不断增长。这两个项目最终都将减少飞行或生产能源所需的燃料，从而减少排放，增强可持续性，降低运营成本。具体地说，该项目重点研究导致跨音速涡轮机效率损失的机制。跨音速涡轮级可以实现比亚音速级更大的功提取，因此可能会导致所需的级更少，重量更轻，但由于各种流体力学现象，通常效率较低。特别重要的是从上游叶片排脱落的周期性尾迹在后续叶片排内的流动中的动力学。因此，本项目将明确测量跨音速涡轮叶片内由于尾迹通过而产生的损失机理。为此，粒子图像测速仪(PIV)将应用于配备了高速移动杆装置的大型跨音速线性叶栅装置。该设施位于通用电气全球研究中心，可以复制真实喷气发动机中的条件，但以一种易于处理的方式进行高级测量。多伦多大学的教职员工和学生将在这个设施上进行几项实验活动。该项目涉及几个创新方面。将在PIV诊断方面做大量的开发工作，以便能够测量实验中发现的全部动态速度范围，并获得涡轮叶片附近边界层的测量结果。此外，新的数据挖掘技术将被用于明确计算湍流动能的传输，从而识别导致效率损失的区域。

This project will enable gas turbines engines with improved efficiency and reduced weight. These engines areubiquitous in aviation, and also form a large and growing portion of the energy landscape. Both of the projectobjectives will ultimately result in less fuel required to fly or produce energy, and hence reduced emissions,greater sustainability, and lower operating costs.Specifically, this project focuses on mechanisms causing efficiency losses in transonic turbines. Transonicturbine stages can achieve greater work extraction than subsonic stages, and hence can potentially result infewer required stages and reduced weight, but also typically are less efficient due to various fluid mechanicphenomena. Of particular importance is the dynamics of periodic wakes shed from upstream rows of blades onthe flow inside a subsequent blade row. This project therefore will explicitly measure the loss mechanismsoccurring inside transonic turbine blades due to the passage of wakes.To do so, particle image velocimetry (PIV) will be applied in a large transonic linear cascade facility thathas been equipped with a high-speed moving bar apparatus. The facility, located at the GE Global ResearchCenter, can replicate the conditions found in real jet engines, but in a manner that is tractable for advancedmeasurements. Several experimental campaigns will be performed by University of Toronto staff and studentsat this facility.The project involves several innovative aspects. Considerable developmental work will be done on the PIVdiagnostics to enable measurement of the full dynamic velocity range found in the experiments, and to obtainmeasurements in the boundary layers near the turbine blades. Furthermore, new data mining techniques will beemployed to explicitly calculate transport of turbulence kinetic energy, and hence identify regions contributingto efficiency losses.